Iterated Posterior Linearization PMB Filter for 5G SLAM
This addresses a specific bottleneck in 5G SLAM for improved localization and mapping, but is incremental as it modifies an existing filter approach.
The paper tackles the nonlinearity in 5G SLAM measurement models by linearizing with respect to the posterior PDF instead of the prior, implementing this in a Poisson multi-Bernoulli filter. Simulation results show improvements in accuracy and precision.
5G millimeter wave (mmWave) signals have inherent geometric connections to the propagation channel and the propagation environment. Thus, they can be used to jointly localize the receiver and map the propagation environment, which is termed as simultaneous localization and mapping (SLAM). One of the most important tasks in the 5G SLAM is to deal with the nonlinearity of the measurement model. To solve this problem, existing 5G SLAM approaches rely on sigma-point or extended Kalman filters, linearizing the measurement function with respect to the prior probability density function (PDF). In this paper, we study the linearization of the measurement function with respect to the posterior PDF, and implement the iterated posterior linearization filter into the Poisson multi-Bernoulli SLAM filter. Simulation results demonstrate the accuracy and precision improvements of the resulting SLAM filter.